Machine Learning Skills you will learn

  • Supervised and unsupervised learning
  • Time series modeling
  • Linear and logistic regression
  • Kernel SVM
  • KMeans clustering
  • Naive Bayes
  • Decision tree
  • Random forest classifiers
  • Boosting and Bagging techniques
  • Deep Learning fundamentals

Who should learn this free Machine Learning course?

  • Analytics Managers
  • Business Analysts
  • Information Architects
  • Developers

What you will learn in this free Machine Learning course?

  • Machine Learning using Python

    • Lesson 01 - Course Introduction

      • 1.01 Course Introduction
        02:39
      • 1.02 What You Will Learn
        01:55

    • Lesson 02 - Introduction to Machine Learning

      • 2.01 Introduction
        00:51
      • 2.02 What Is Machine Learning?
        02:49
      • 2.03 Types of Machine Learning
        02:54
      • 2.04 Machine Learning Pipeline and MLOP's
        03:35
      • 2.05 Introduction to Python Packages Used in Machine Learning
        03:29
      • 2.06 Recap
        00:52

    • Lesson 03 - Supervised Learning

      • 3.01 Introduction
        00:41
      • 3.02 Supervised Learning
        02:34
      • 3.03 Applications of Supervised Learning
        03:11
      • 3.04 Preparing and Shaping Data
        06:50
      • 3.05 What is overfitting and underfitting?
        02:23
      • 3.06 Detecting and Preventing Overfitting and Underfitting
        07:36
      • 3.07 Regularization
        02:38
      • 3.08 Recap
        00:41

    • Lesson 04 - Regression and Applications

      • 4.01 Introduction
        01:14
      • 4.02 What is Regression?
        01:34
      • 4.03 Regression Types: Introduction
        02:45
      • 4.04 Linear Regression
        02:48
      • 4.05 Working with Linear Regression
        35:14
      • 4.06 Critical Assumptions for Linear Regression
        01:31
      • 4.07 Logistic Regression
        02:33
      • 4.08 Data Exploration Using SMOTE
        12:56
      • 4.09 Over Sampling Using SMOTE
        01:48
      • 4.10 Polynomial Regression
        02:41
      • 4.11 Data Preparation Model Building and Performance Evaluation Part A
        04:53
      • 4.12 Ridge Regression
        01:57
      • 4.13 Data Preparation Model Building: Part B
        06:25
      • 4.14 LASSO Regression
        02:30
      • 4.15 Data Preparation Model Building: Part C
        06:13
      • 4.16 Recap
        00:55
      • 4.17 Spotlight
        02:40

    • Lesson 05 - Classification and Applications

      • 5.01 Introduction
        01:03
      • 5.02 What are Classification Algorithms?
        02:09
      • 5.03 Types of Classification
        03:29
      • 5.04 Types and selection of performance parameters
        04:58
      • 5.05 Naive Bayes
        02:56
      • 5.06 Applying Naive Bayes Classifier
        03:27
      • 5.07 Stochastic Gradient Descent
        03:25
      • 5.08 Applying Stochastic Gradient Descent
        05:02
      • 5.09 K Nearest Neighbors
        02:41
      • 5.10 Applying K Nearest Neighbors
        05:28
      • 5.11 Decision Tree
        02:42
      • 5.12 Applying Decision Tree
        04:27
      • 5.13 Random Forest
        01:59
      • 5.14 Applying Random Forest
        03:22
      • 5.15 Boruta Explained
        01:15
      • 5.16 Automatic Feature Selection with Boruta
        06:43
      • 5.17 Support Vector Machine
        02:27
      • 5.18 Applying Support Vector Machine
        07:07
      • 5.19 Cohens Kappa Measure
        01:22
      • 5.20 Recap
        00:42

    • Lesson 06 - Unsupervised Algorithms

      • 6.01 Introduction
        00:53
      • 6.02 What are Unsupervised Algorithms?
        02:51
      • 6.03 Types of Unsupervised Algorithms Clustering and Associative
        02:15
      • 6.04 When to Use Unsupervised Algorithms?
        01:22
      • 6.05 Visualizing Outputs
        06:14
      • 6.06 Performance Parameters
        02:55
      • 6.07 Clustering Types
        00:56
      • 6.08 Hierarchical Clustering
        03:32
      • 6.09 Applying Hierarchical Clustering
        03:22
      • 6.10 K means Clustering: Part 1
        02:30
      • 6.11 K means Clustering: Part 2
        01:54
      • 6.12 Applying K Means Clustering
        03:37
      • 6.13 KNN-K Nearest Neighbors
        03:41
      • 6.14 Outlier Detection
        01:47
      • 6.15 Outlier Detection Algorithms in PyOD
        02:49
      • 6.16 Demo: K NN for Anomaly Detection
        02:37
      • 6.17 Principal Component Analysis
        04:15
      • 6.18 Applying Principal Component Analysis: PCA
        04:21
      • 6.19 Correspondence Analysis Multiple correspondence analysis: MCA
        03:16
      • 6.20 Singular Value Decomposition
        02:06
      • 6.21 Applying Singular Value Decomposition
        04:14
      • 6.22 Independent Component Analysis
        02:26
      • 6.23 Applying Independent Component Analysis
        01:54
      • 6.24 BIRCH
        02:33
      • 6.25 Applying BIRCH
        02:15
      • 6.26 Recap
        01:05
      • 6.27 Spotlight
        03:20

    • Lesson 07 - Ensemble Learning

      • 7.01 Introduction
        00:54
      • 7.02 What is Ensemble Learning?
        01:46
      • 7.03 Categories in Ensemble Learning
        02:47
      • 7.04 Sequential Ensemble Technique
        02:50
      • 7.05 Parallel Ensemble Technique
        02:10
      • 7.06 Types of Ensemble Methods
        01:56
      • 7.07 Bagging
        03:01
      • 7.08 Demo: Bagging
        02:53
      • 7.09 Boosting
        02:14
      • 7.10 Demo: Boosting
        03:29
      • 7.11 Stacking
        02:56
      • 7.12 Demo: Stacking
        03:44
      • 7.13 Reducing Errors with Ensembles
        05:27
      • 7.14 Applying Averaging and Max Voting
        06:33
      • 7.15 Hello World Tensorflow
        02:38
      • 7.16 Hands on with TensorFlow: Part A
        05:09
      • 7.17 Keras
        02:49
      • 7.18 Hands on with TensorFlow: Part B
        05:57
      • 7.19 Recap
        00:45

    • Lesson 08 - Recommender System

      • 8.01 Introduction
        01:00
      • 8.02 How do recommendation engines work
        02:45
      • 8.03 Recommendation Engine: Use Cases
        01:44
      • 8.04 Examples of Recommender System and Their Designs
        02:55
      • 8.05 Leveraging PyTorch to Build a Recommendation Engine
        02:23
      • 8.06 Collaborative Filtering and Memory Based Modeling
        06:31
      • 8.07 Item Based Collaborative Filtering
        07:02
      • 8.08 User Based Collaborative Filtering
        13:05
      • 8.09 Model Based Collaborative Filtering
        04:09
      • 8.10 Dimensionality Reduction and Matrix Factorization
        04:51
      • 8.11 Accuracy Matrices in ML
        08:06
      • 8.12 Recap
        00:52
      • 8.13 Spotlight
        03:01

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Learn the Basics of Machine Learning

Why you should learn Machine Learning?

$105.45 billion

Expected machine learning market growth by 2025

44.1% growth

In the adoption of machine learning in organizations

Career Opportunities

About the Course

This free machine learning course is designed to provide you with a solid foundation in machine learning, one of the most exciting and rapidly growing fields in technology and data science. Whether you're a beginner or a machine learning professional looking to refresh your knowledge, this course is just the right one for you!

What are the Topics Covered in this Course?

This course introduces key machine le

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FAQs

  • Is there a cost associated with this free Machine Learning course?

    No, this course is completely free, and you will receive a certificate upon completion.

  • What are the prerequisites to learn this free course on Machine Learning?

    There are no strict prerequisites. Basic programming knowledge and a passion for learning are helpful.

  • When can I expect to receive my certificate?

    You'll receive your certificate upon successfully completing the course.

  • What is the duration of my access to the course?

    Your access to the course is unlimited, so you can learn at your own pace.

  • How difficult is this free Machine Learning course?

    The course is designed to cater to learners of all levels, from beginners to advanced. It provides a structured learning path to make it accessible to everyone.

  • Is ML easier than AI?

    Machine learning is a subset of artificial intelligence (AI). AI encompasses a broader range of topics, while machine learning focuses on algorithms that enable computers to learn from data.

  • What is the salary of a Machine Learning engineer?

    Salaries can vary widely based on location and experience. On average, machine learning engineers earn competitive salaries due to the high demand for their skills.

  • What is the future scope of machine learning?

    Machine learning is a rapidly evolving field with a promising future. It's being applied across industries like healthcare, finance, and technology, making it a key driver of innovation and job opportunities.

  • What is the difference between machine learning, deep learning, and artificial intelligence?

    AI is the broad concept of machines mimicking human intelligence; machine learning is a subset where systems learn from data, and deep learning is a specialized form of ML using neural networks.

  • What are the common applications of machine learning in various industries?

    ML is used in fraud detection, recommendation systems, healthcare diagnostics, customer segmentation, and predictive maintenance, among others.

  • What programming languages are commonly used in machine learning?

    Python is the most popular language, followed by R, Java, Julia, and C++, due to their libraries and ML frameworks.

  • What is the difference between supervised, unsupervised, and reinforcement learning?

    Supervised learning uses labeled data, unsupervised learning works with unlabeled data to find patterns, and reinforcement learning involves learning through trial and error with feedback.

  • What are the ethical considerations in machine learning?

    Key concerns include data privacy, bias in algorithms, fairness, transparency, and responsible AI usage.

  • What are the common challenges faced when implementing machine learning models?

    Challenges include insufficient or poor-quality data, overfitting, lack of interpretability, and scalability issues.

  • How does machine learning relate to data science?

    Machine learning is a core part of data science, used to build models that extract insights and make predictions from data.

  • What are the best practices for preparing data for machine learning?

    These include cleaning, handling missing values, normalizing, encoding categorical variables, and splitting data into training and test sets.

  • What is model overfitting and how can it be prevented?

    Overfitting occurs when a model learns noise instead of patterns; prevent it with cross-validation, regularization, and using simpler models or more data.

  • What are the career prospects and average salaries for machine learning professionals?

    ML professionals are in high demand, with salaries ranging from ₹8–25 LPA in India and $90K–$150K globally, depending on experience and role.

Learner Review

  • M Ehsani

    M Ehsani

    Thanks to Simplilearn for providing such an insightful course on Machine Learning. Looking forward to applying my learnings in a real-world project.

  • Jyoti Dange

    Jyoti Dange

    The course was really good. I am thorough with the fundamentals of Machine Learning and I have recommended this course to my friends.

  • Rajeev Gaur

    Rajeev Gaur

    The course gave me a lot of exposure to the practical side of Machine Learning projects. It was an awesome experience.

  • Daren Lee

    Daren Lee

    The course material covered concepts with clarity through real-life examples.

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